Zhikun Li — accepted AI-conference papers

2026

Scalable Solution Methods for Dec-POMDPs with Deterministic Dynamics

Yang You, Alex Schutz, Zhikun Li, Bruno Lacerda, Robert Skilton, Nick Hawes

AAAI 2026technical

Many high-level multi-agent planning problems, such as multi-robot navigation and path planning, can be modeled with deterministic actions and observations. In this work, we focus on such domains and introduce the class of Deterministic Decentralized POMDPs (Det-Dec-POMDPs)—a subclass of Dec-POMDPs

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2025

NOVA: An Iterative Planning Framework for Enhancing Scientific Innovation with Large Language Models

Xiang Hu, Hongyu Fu, Jinge Wang, Yifeng Wang, Zhikun Li, Renjun Xu +4

ACL 2025finding

Scientific innovation is pivotal for humanity, and harnessing large language models (LLMs) to generate research ideas could transform discovery. However, existing LLMs often produce simplistic and repetitive suggestions due to their limited ability in acquiring external knowledge for innovation. To…

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